# CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition

arXiv: https://arxiv.org/abs/1904.02141 · HTML: https://mlsanity.com/paper/1904.02141/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter](https://mlsanity.com/paper/2105.07148/) (2021)
- [MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition](https://mlsanity.com/paper/2107.05418/) (2021)
- [Hero-Gang Neural Model For Named Entity Recognition](https://mlsanity.com/paper/2205.07177/) (2022)
- [Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF](https://mlsanity.com/paper/2211.04759/) (2022)
- [American Sign Language to Text Translation using Transformer and Seq2Seq with LSTM](https://mlsanity.com/paper/2409.10874/) (2024)

## Abstract (arXiv)

Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually considered as the first step for Chinese NER. However, models based on word-level embeddings and lexicon features often suffer from segmentation errors and out-of-vocabulary (OOV) words. In this paper, we investigate a Convolutional Attention Network called CAN for Chinese NER, which consists of a character-based convolutional neural network (CNN) with local-attention layer and a gated recurrent unit (GRU) with global self-attention layer to capture the information from adjacent characters and sentence contexts. Also, compared to other models, not depending on any external resources like lexicons and employing small size of char embeddings make our model more practical. Extensive experimental results show that our approach outperforms state-of-the-art methods without word embedding and external lexicon resources on different domain datasets including Weibo, MSRA and Chinese Resume NER dataset.